Dependency Based Bilingual word Embeddings without word alignment

In this work, we trained different bilingual word embeddings models without word alignments (BilBOWA) using linear Bag-of-words contexts and dependency-based contexts. BilBOWA embedding models learn distributed representations of words by jointly optimizing a monolingual and a bilingual objective. Including dependency features in the monolingual objective, improves the accuracy of learning bilingual word embeddings up to 6% points in English-Spanish (En-Es) and up to 2.5% points in English-German (En-De) language pairs in word translation task compared to the baseline model. However, using these dependency features in both monolingual and bilingual objectives does not lead to any improvement in the En-Es language pair and only shows minor improvement for En-De. Moreover, our results provide evidence that using dependency features in bilingual word embeddings has a different effect based on syntactic and sentence structure similarity of the language pair.